Range-adaptive feature extractor.
In [1]}, a spatially-adaptive convolutions (SAC) is adopted to achieve more efficient point cloud segmentation. Intuitively, it also could be a powerful tool for our feature extractor for range images. Therefore, we extract the SAC module (Fig. REF ) to incorporate it into our feature extractor. As shown in Fig. REF  (bottom), we adopt the convolutional layer with the \(3\times 3\)  kernel followed by three SAC modules, where Leaky ReLu (\(\alpha \)  \(=\)  0.1) is utilized after each convolutional layer. Note that there is not any normalization layer, e.g., batch normalization, since it has been proven that the batch normalization is not suited for image quality improvement [2]}. We jointly train the feature extraction network with our entire model, which learns range features adaptively for point cloud frame interpolation.
